Model Training & Adaptation
Causal inference
Drawing conclusions about how one factor changes another.
Example
An analysis estimates whether a new training program caused a performance improvement.
Why people use it
It helps separate an action's effects from changes that would have happened anyway.
What you'll hear
“Did the training cause the improvement?”
What this means for you
Look for an appropriate study design and treatment of confounding factors.
Can you control it?
Developer-only
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Can a strong pattern prove cause and effect?
- No. Other influences or the way examples were selected may explain why the two things appear linked.
- Why is a comparison group useful?
- It helps show what might have happened without the action being studied.
- Can timing alone prove a cause?
- No. One event happening before another does not establish that it caused the later event.